Students’ emotions and attitudes are discernible in messages posted to online question and answer boards. Understanding student sentiment may help instructors identify students with potential course issues, optimize help-seeking, and potentially improve student achievement, as well as identify both positive and negative actions by instructors and provide them with valuable feedback. Towards this end, we present a set of context-independent emotion acts that were used by students in a university-level computer science course to express certainty and uncertainty, frustration, and politeness in an online Q&A board and develop viable classification approaches. To explore the potential of sentiment-based profiling, we present a heuristic-driven analysis of thread resolution and detail future research.